{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "8804bc60-c9e2-4713-9800-1bc2fea11241",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/umesh/conda/lib/python3.10/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
      "  from .autonotebook import tqdm as notebook_tqdm\n"
     ]
    }
   ],
   "source": [
    "from transformers import pipeline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "322cd68e-9257-4d07-aafc-19e9072f1bba",
   "metadata": {},
   "outputs": [],
   "source": [
    "from transformers import AutoModelForSequenceClassification, AutoTokenizer\n",
    "\n",
    "model = AutoModelForSequenceClassification.from_pretrained('.')\n",
    "tokenizer = AutoTokenizer.from_pretrained('.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "fde22c8f-4d57-4544-bc2b-55e4dbbc5fd3",
   "metadata": {},
   "outputs": [],
   "source": [
    "classifier = pipeline('sentiment-analysis',model = model, tokenizer = tokenizer, top_k =1)\n",
    "det_classer = pipeline('sentiment-analysis',model = model, tokenizer = tokenizer, top_k =None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "087c3506-5d27-4dd6-9377-fb34e6926698",
   "metadata": {},
   "outputs": [],
   "source": [
    "inputs=['This is a rather confusing statement','I do not want to bee seen with you Kusakabe','Rin chan was elegant in dismissing your favours','Damn you! Bonn-kun']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "f5683415-239a-4e68-8f4c-1b9ba8295787",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[{'label': 'DISGUST', 'score': 0.2899283468723297}],\n",
       " [{'label': 'DISGUST', 'score': 0.20709046721458435}],\n",
       " [{'label': 'OPTIMISM', 'score': 0.1878553181886673}],\n",
       " [{'label': 'ANGER', 'score': 0.46816307306289673}]]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classifier(inputs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "9b45cd86-cd63-40b1-81ff-7b510ac94c8d",
   "metadata": {},
   "outputs": [],
   "source": [
    "statement='Today I did not finish my project and stayed silently away from meetings but my boss found it out and shouted at me in front of a female colleague '"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "3ae20452-e327-4550-a40d-f28280aad3c8",
   "metadata": {},
   "outputs": [],
   "source": [
    "x = det_classer(statement)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "ba091f5c-b98c-49ee-a361-f8937c3693c1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'DISGUST'"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x[0][0]['label']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "003693e3-1aac-4d67-9c54-f17bcda1af34",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'DISGUST': 0.3289419114589691}\n",
      "{'ANGER': 0.2868015170097351}\n",
      "{'SADNESS': 0.12498752772808075}\n",
      "{'FEAR': 0.09882446378469467}\n",
      "{'ANTICIPATION': 0.051536574959754944}\n",
      "{'JOY': 0.03244535252451897}\n",
      "{'SURPRISE': 0.023728473111987114}\n",
      "{'PESSIMISM': 0.020501434803009033}\n",
      "{'OPTIMISM': 0.020457664504647255}\n",
      "{'COMPLICATED': 0.006496347486972809}\n",
      "{'TRUST': 0.0026527740992605686}\n",
      "{'LOVE': 0.0026260693557560444}\n"
     ]
    }
   ],
   "source": [
    "for a in x[0]:\n",
    "    print ({a['label']:a['score']})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "a7361b72-d959-4993-80cf-5c0d65f3e703",
   "metadata": {},
   "outputs": [],
   "source": [
    "def classify (statement):\n",
    "    preds = det_classer(statement)\n",
    "    return {i['label']:float(i['score']) for i in preds[0]}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "9b6bff6a-3a70-48d8-8b03-1d8327278cad",
   "metadata": {},
   "outputs": [],
   "source": [
    "import gradio as gr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "d1aa90b3-678f-4ae0-bfa7-6018034c5b3e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running on local URL:  http://127.0.0.1:7862\n",
      "IMPORTANT: You are using gradio version 4.26.0, however version 4.29.0 is available, please upgrade.\n",
      "--------\n",
      "\n",
      "To create a public link, set `share=True` in `launch()`.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div><iframe src=\"http://127.0.0.1:7862/\" width=\"100%\" height=\"500\" allow=\"autoplay; camera; microphone; clipboard-read; clipboard-write;\" frameborder=\"0\" allowfullscreen></iframe></div>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": []
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "demo = gr.Interface(fn=classify, inputs='text',outputs=gr.Label())\n",
    "demo.launch()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e7852458-9afc-4443-bfc8-ebd16909dc6a",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}